Optical navigation upon arbitrary surfaces produces motion signals indicative of relative movement along the directions of coordinate axes, and is becoming increasingly prevalent. It is used, for instance, in optical computer mice and fingertip tracking devices to replace conventional mice and trackballs for the position control of screen pointers in windowed user interfaces for computer systems. It has many advantages, among which are the lack of moving parts that accumulate dirt and suffer the mechanical wear and tear of use. Another advantage of an optical mouse is that it does not need a mouse pad, since it is generally capable of navigating upon arbitrary surfaces, so long as they are not optically featureless.
Optical navigation operates by tracking the relative displacement between two images. To determine the relative displacement between two images, a surface is illuminated and a two-dimensional view of a portion of the surface is focused upon an array of photodetectors. The output of the photodetectors is digitized and stored as a reference image in a corresponding array of memory. A brief time later a sample image is captured using the same process. If there has been no motion between the image capture events, then the sample image and the reference image are identical (or very nearly so). That is, the image features of the reference image data and the sample image data appear to match up. If, on the other hand, there has been some motion between the image capture events, then the features of the sample image will appear to have shifted within its borders, and the digitized arrays will no longer match. The matching process that is used in optical navigation to align similar features of two images is termed “correlation” and typically involves a two-dimensional cross-correlation between the reference image data and the sample image data. A two-dimensional cross-correlation between the reference image data and the sample image data compares intensity values of the image data on a pixel-by-pixel basis to determine relative displacement between the two sets of image data.
The image features that are relied upon to determine relative displacement are produced by illuminating a surface. If the illumination of the surface is not evenly distributed or the illumination source is not properly aligned, tracking errors may result. In particular, a misaligned illumination source can cause boarders of the image data to appear dark and therefore lack sufficient contrast to support the feature matching process. Further, if the illumination drops off suddenly at some point within the image data, the contrast in brightness may appear as an image feature (e.g., an edge), which can severely degrade the tracking efficiency. The false detection of an edge can be especially detrimental in image tracking algorithms that rely on edge detection.
In view of this, what is needed is a technique for optical navigation that addresses navigation errors that are caused by poor illumination.